class_docstr: Container for cluster load metrics.
  
  Metrics here are updated from raylet heartbeats. The autoscaler
  queries these metrics to determine when to scale up, and which nodes
  can be removed.
code: def get_resource_demand_vector(self, clip=True):
      if clip:
          # Bound the total number of bundles to
          # 2xMAX_RESOURCE_DEMAND_VECTOR_SIZE. This guarantees the resource
          # demand scheduler bin packing algorithm takes a reasonable amount
          # of time to run.
          return (
              self.waiting_bundles[
                  :AUTOSCALER_MAX_RESOURCE_DEMAND_VECTOR_SIZE
              ]
              + self.infeasible_bundles[
                  :AUTOSCALER_MAX_RESOURCE_DEMAND_VECTOR_SIZE
              ]
          )
      else:
          return self.waiting_bundles + self.infeasible_bundles
  
imports: import logging
  import time
  from collections import Counter
  from functools import reduce
  from typing import Dict, List
  from ray._private.gcs_utils import PlacementGroupTableData
  from ray.autoscaler._private.constants import AUTOSCALER_MAX_RESOURCE_DEMAND_VECTOR_SIZE, AUTOSCALER_REPORT_PER_NODE_STATUS
  from ray.autoscaler._private.util import DictCount, LoadMetricsSummary, NodeIP, ResourceDict
  from ray.core.generated.common_pb2 import PlacementStrategy
label_desc: Use image node_id 11 for calling the LoadMetrics obj's underlying member method code with example usage: obj.get_resource_demand_vector(clip) and returns: unknown, unknown